We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these...
Christopher J. C. Burges, Tal Shaked, Erin Renshaw...
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
Standard Machine Learning approaches to text classification use the bag-of-words representation of documents to deceive the classification target function. Typical linguistic stru...
In this paper, we present a simple yet efficient automatic system to translate biomedical terms. It mainly relies on a machine learning approach able to infer rewriting rules from...
The definition of new concepts or roles for which extensional knowledge become available can turn out to be necessary to make a DL ontology evolve. In this paper we reformulate thi...